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- W2962793900 abstract "Abstract In this paper, we compute the sentiment of social media posts using a novel set of fuzzy rules involving multiple lexicons and datasets. The proposed fuzzy system integrates Natural Language Processing techniques and Word Sense Disambiguation using a novel unsupervised nine fuzzy rule based system to classify the post into: positive, negative or neutral sentiment class. We perform a comparative analysis of our method on nine public twitter datasets, three sentiment lexicons, four state-of-the-art approaches for unsupervised Sentiment Analysis and one state-of-the-art method for supervised machine learning. Traditionally, Sentiment Analysis of twitter data is performed using a single lexicon. Our results can give an insight to researchers to choose which lexicon is best for social media. The fusion of fuzzy logic with lexicons for sentiment classification provides a new paradigm in Sentiment Analysis. Our method can be adapted to any lexicon and any dataset (two-class or three-class sentiment). The experiments on benchmark datasets yield higher performance for our approach as compared to the state-of-the-art." @default.
- W2962793900 created "2019-07-30" @default.
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- W2962793900 date "2019-12-01" @default.
- W2962793900 modified "2023-10-18" @default.
- W2962793900 title "Fuzzy rule based unsupervised sentiment analysis from social media posts" @default.
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- W2962793900 doi "https://doi.org/10.1016/j.eswa.2019.112834" @default.
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